feat: implement streaming support for chat and enhance safety review process

- Updated .env.example to include API key placeholder and configuration instructions.
- Refactored main.py to support streaming responses from the LLM, improving user experience during chat interactions.
- Enhanced LLMClient to include methods for streaming chat and collecting responses.
- Modified safety review process to pass static analysis warnings to the LLM for better code safety evaluation.
- Improved UI components in chat_view.py to handle streaming messages effectively.

llm/client.py

@@ -1,12 +1,14 @@
 """
 LLM 统一调用客户端
 所有模型通过 SiliconFlow API 调用
+支持流式和非流式两种模式
 """
 
 import os
+import json
 import requests
 from pathlib import Path
-from typing import Optional
+from typing import Optional, Generator, Callable
 from dotenv import load_dotenv
 
 # 获取项目根目录
@@ -25,12 +27,19 @@ class LLMClient:
     
     使用方式:
         client = LLMClient()
+        
+        # 非流式调用
         response = client.chat(
             messages=[{"role": "user", "content": "你好"}],
-            model="Qwen/Qwen2.5-7B-Instruct",
-            temperature=0.7,
-            max_tokens=1024
+            model="Qwen/Qwen2.5-7B-Instruct"
         )
+        
+        # 流式调用
+        for chunk in client.chat_stream(
+            messages=[{"role": "user", "content": "你好"}],
+            model="Qwen/Qwen2.5-7B-Instruct"
+        ):
+            print(chunk, end="", flush=True)
     """
     
     def __init__(self):
@@ -49,22 +58,21 @@ class LLMClient:
         messages: list[dict],
         model: str,
         temperature: float = 0.7,
-        max_tokens: int = 1024
+        max_tokens: int = 1024,
+        timeout: int = 180
     ) -> str:
         """
-        调用 LLM 进行对话
+        调用 LLM 进行对话（非流式）
         
         Args:
-            messages: 消息列表，格式为 [{"role": "user/assistant/system", "content": "..."}]
+            messages: 消息列表
             model: 模型名称
-            temperature: 温度参数，控制随机性
+            temperature: 温度参数
             max_tokens: 最大生成 token 数
+            timeout: 超时时间（秒），默认 180 秒
             
         Returns:
             LLM 生成的文本内容
-            
-        Raises:
-            LLMClientError: 网络异常或 API 返回错误
         """
         headers = {
             "Authorization": f"Bearer {self.api_key}",
@@ -84,10 +92,10 @@ class LLMClient:
                 self.api_url,
                 headers=headers,
                 json=payload,
-                timeout=60
+                timeout=timeout
             )
         except requests.exceptions.Timeout:
-            raise LLMClientError("请求超时，请检查网络连接")
+            raise LLMClientError(f"请求超时（{timeout}秒），请检查网络连接或稍后重试")
         except requests.exceptions.ConnectionError:
             raise LLMClientError("网络连接失败，请检查网络设置")
         except requests.exceptions.RequestException as e:
@@ -109,6 +117,121 @@ class LLMClient:
             return content
         except (KeyError, IndexError, TypeError) as e:
             raise LLMClientError(f"解析 API 响应失败: {str(e)}")
+    
+    def chat_stream(
+        self,
+        messages: list[dict],
+        model: str,
+        temperature: float = 0.7,
+        max_tokens: int = 2048,
+        timeout: int = 180
+    ) -> Generator[str, None, None]:
+        """
+        调用 LLM 进行对话（流式）
+        
+        Args:
+            messages: 消息列表
+            model: 模型名称
+            temperature: 温度参数
+            max_tokens: 最大生成 token 数
+            timeout: 超时时间（秒）
+            
+        Yields:
+            逐个返回生成的文本片段
+        """
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json"
+        }
+        
+        payload = {
+            "model": model,
+            "messages": messages,
+            "stream": True,
+            "temperature": temperature,
+            "max_tokens": max_tokens
+        }
+        
+        try:
+            response = requests.post(
+                self.api_url,
+                headers=headers,
+                json=payload,
+                timeout=timeout,
+                stream=True
+            )
+        except requests.exceptions.Timeout:
+            raise LLMClientError(f"请求超时（{timeout}秒），请检查网络连接或稍后重试")
+        except requests.exceptions.ConnectionError:
+            raise LLMClientError("网络连接失败，请检查网络设置")
+        except requests.exceptions.RequestException as e:
+            raise LLMClientError(f"网络请求异常: {str(e)}")
+        
+        if response.status_code != 200:
+            error_msg = f"API 返回错误 (状态码: {response.status_code})"
+            try:
+                error_detail = response.json()
+                if "error" in error_detail:
+                    error_msg += f": {error_detail['error']}"
+            except:
+                error_msg += f": {response.text[:200]}"
+            raise LLMClientError(error_msg)
+        
+        # 解析 SSE 流
+        for line in response.iter_lines():
+            if line:
+                line = line.decode('utf-8')
+                if line.startswith('data: '):
+                    data = line[6:]  # 去掉 "data: " 前缀
+                    if data == '[DONE]':
+                        break
+                    try:
+                        chunk = json.loads(data)
+                        if 'choices' in chunk and len(chunk['choices']) > 0:
+                            delta = chunk['choices'][0].get('delta', {})
+                            content = delta.get('content', '')
+                            if content:
+                                yield content
+                    except json.JSONDecodeError:
+                        continue
+    
+    def chat_stream_collect(
+        self,
+        messages: list[dict],
+        model: str,
+        temperature: float = 0.7,
+        max_tokens: int = 2048,
+        timeout: int = 180,
+        on_chunk: Optional[Callable[[str], None]] = None
+    ) -> str:
+        """
+        流式调用并收集完整结果
+        
+        Args:
+            messages: 消息列表
+            model: 模型名称
+            temperature: 温度参数
+            max_tokens: 最大生成 token 数
+            timeout: 超时时间（秒）
+            on_chunk: 每收到一个片段时的回调函数
+            
+        Returns:
+            完整的生成文本
+        """
+        full_content = []
+        
+        for chunk in self.chat_stream(
+            messages=messages,
+            model=model,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            timeout=timeout
+        ):
+            full_content.append(chunk)
+            if on_chunk:
+                on_chunk(chunk)
+        
+        return ''.join(full_content)
 
 
 # 全局单例（延迟初始化）
@@ -121,4 +244,3 @@ def get_client() -> LLMClient:
     if _client is None:
         _client = LLMClient()
     return _client
-